Maria Lomeli

About me

I am a research engineer at Fundamental AI Research, Meta. Previously, I was a senior research scientist at Babylon Health, UK. Before that, I was a research associate, working with Zoubin Ghahramani at the Machine Learning group, CBL, University of Cambridge and member of Trinity Hall college. I studied my PhD at the Gatsby Unit, UCL, my supervisor was Yee Whye Teh.

Journal publications

  • Petroni, F., Broscheit, S., Piktus, A., Lewis, P., Izacard, G., Hosseini, L., Dwivedi-Yu, J., Lomeli, M., Schick, T., Mazaré, P.E., Joulin, A., Grave, E., Riedel, S., ''Improving wikipedia verifiability with AI'' Nature Machine Intelligence, 2023, Vol 5, pp 1142–1148, NMI.
  • Izacard, G., Lewis, P., Lomeli, M., Hosseini, L., Petroni, F., Schick, T., Dwivedi-Yu, J., Joulin, A., Riedel, S., Grave, E., ''Atlas: few-shot learning with retrieval-augmented language models'' Journal of Machine Learning Research, 2023, Vol 24, pp 1-43, JMLR.
  • Mialon, G., Dessi, R., Lomeli, M., Nalmpantis, C., Pasunuru, R., Raileanu, R., Rosiere, B., Schick, T., Dwivedi-Yu, J., Celikyilmaz, A., LeCun, Y., Scialom, T., ''Augmented language models: a survey'', Transactions of Machine Learning Research, 2023, Vol 6, TLMR .
  • Valera, I., Pradier, M., Lomeli, M. and Ghahramani, Z., ''General Latent Feature Model for Heterogeneous Datasets'', Journal of Machine Learning Research, 2020, Vol 21 JMLR.
  • Lomeli, M., Rowland, M., Gretton, A. and Ghahramani, Z., ''Antithetic and Monte Carlo kernel estimators for partial rankings'', Statistics and Computing, 2019, Vol 29,1127–1147, StCo.
  • Lomeli, M., Favaro, S., Teh, Y. W.,'' A marginal sampler for -Stable Poisson-Kingman mixture models'', Journal of Computational and Graphical Statistics, 2017, Vol 26,pp 44-53 JCGS.
  • Favaro, S., Lomeli, M., Nipoti, B., Teh, Y.W., ''Stick-breaking representations of -stable Poisson-Kingman models'' , Electronic Journal of Statistics, 2014, Vol. 8, pp 1063-1085 EJS.
  • Favaro, S., Lomeli, M., Teh, Y.W.,''On a class of -stable Poisson-Kingman models and an effective marginalized sampler'', Statistics and Computing, 2014, Vol 25, pp 67-78 StCo.


  • Lin, V. X., Chen, X., Chen, M., Shi, W., Lomeli, M., James, R., Rodriguez, P., Kahn, J., Szilvasy, G., Lewis, M., Zettlemoyer, L., Yih, S., 2024. ''RA-DIT: Retrieval-Augmented Dual Instruction Tuning'' ICLR
  • Shi, W., Min, S., Lomeli, M., Chou, Z., Li, M., Lin, V., Smith, N. A., Zettlemoyer, L., Yih, S., Lewis, M. 2024, ''In-Context Pretraining: Language Modeling Beyond Document Boundaries'' ICLR (accepted as spotlight)
  • Schick, T., Dwivedi-Yu, J., Dessi, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T., 2023, ''Toolformer: language models can teach themselves to use tools'' Neural Information Processing Systems NeurIPS (accepted as an oral presentation)
  • Lomeli, M., Favaro, S.,Teh, Y.W., 2015, ''A hybrid sampler for Poisson-Kingman mixture models'', Neural information Processing Systems NeurIPS
  • Sejdinovic, D., Strathmann, H., Lomeli Garcia, M., Andrieu, C., Gretton, A., 2014,''Kernel Adaptive Metropolis-Hastings'', International Conference in Machine Learning ICML
  • Harman, M., Ahlgren, J., Berezin, M., Dulskyte, E., Dvortsova, I., George, J., Gucevska, N., Meijer, E., Spahr-Summers, J., Bojarczuk, K., Sapora, S. and Lomeli, M., 2021, ''Testing Web Enabled Simulation at Scale Using Metamorphic Testing'', International Conference on Software Engineering ICSE


  • Gautam, D., Lomeli, M., Gourgoulias, K., Thompson, D., Johri, S., 2019, ''Masking schemes for universal marginalisers'', Advances in Approximate Bayesian Inference symposium
  • Bloem-Reddy, B., Mathieu, E., Foster, A., Rainforth, T., Ge, H., Lomeli, M., Ghahramani, Z., Teh, Y.W., 2017, ''Sampling and inference for discrete random probability measures in probabilistic programs'', Advances in Approximate Bayesian Inference workshop, NeurIPS


  • General Bayesian inference schemes in infinite mixture models
    PhD thesis, University College London
    UCL repository: Doctoral dissertation link


  • Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazare, P.E., Lomeli, M., Hosseini, L., Jegou, H. 2024, ''The faiss library'' arXiv
  • Dwivedi-Yu, J., Schick, T., Jiang, Z., Lomeli, M., Lewis, P., Izacard, G., Grave, E., Riedel, S., Petroni, F., 2022, ''EditEval: an instruction-based benchmark for text omprovements'' arXiv


  • February, 2024. Talk at the RIIA 6.0 summer school, Quito, Ecuador (virtual)
  • July, 2023. Talk at the Microsoft Research Montreal and Mila seminar, Montreal, Canada (virtual)
  • June, 2023. Talk at the Gatsby unit anniversary symposium, UCL, London
  • June, 2023. Talk at the virtual seminar series Sinc instutite, Santa Fe, Argentina (virtual)
  • March, 2023. Talk at the NLP parallel session, Latin American Meeting in Artificial Intelligence, Khipu, Montevideo, Uruguay
  • October, 2021. Joint keynote talk with Mark Harman at the ESEM 2021 conference (virtual)
  • September, 2019. Talk at the ''Recent developments on kernel methods'', UCL, London
  • July, 2019. Talk at the Gatsby unit anniversary symposium, UCL, London
  • June, 2019. Talk at ''Congreso Bayesiano de América Latina'', Lima, Peru
  • April, 2019. Talk at ''Achieving impact in healthcare: from mathematics to clinical support systems and devices'' workshop, Newton Institute, Cambridge, UK
  • March 20, 2019. Talk at the Statistics seminar series, Queen Mary University, London
  • June 11, 2018. Talk at Parallelising Monte Carlo Algorithms workshop, School of Mathematics, University of Bristol, Bristol
  • March 15, 2018. Talk at the CamAIML event, Microsoft research Cambridge, Cambridge, UK
  • February 16, 2018. Talk at the University of Glasgow, Statistics seminar, Glasgow, UK
  • Febryary 2, 2018. Talk at UCL, CSML Lunchtime seminar, London, UK
  • February 1, 2018. Talk at Amazon Cambridge research series seminar, Cambridge, UK
  • August 30, 2017. Talk at the 2017 SMC workshop, Uppsala, Sweden
  • June 7, 2017. Talk at the ''Congreso Bayesiano de América Latina'', Guanajuato, Mexico
  • June 14, 2016. Talk at the ''Bayes Legacy'' sesssion, 13th ISBA Wold meeting in Sardinia, Italy
  • June 2, 2016. Talk at the Machine Learning group, CBL, University of Cambridge
  • May 5, 2016. Talk at Machine Learning reading group, CBL, University of Cambridge
  • July 16, 2015. Talk at CBL, University of Cambridge
  • June 22, 2015. Talk at the 10th Bayesian Nonparametrics conference
  • June 15, 2015. Talk at the 9th Bayesian Inference for Stochastic Processes conference
  • January 26, 2014. Talk at the Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México
  • October 24, 2014. Talk at the Computational Statistics seminar, University of Oxford
  • September 24, 2014. Talk at CBL, University of Cambridge
  • March 3, 2014. Talk at the workshop Advances in Scalable Bayesian Computation, available online here


  • Teaching assistant, Part II Statistical modelling course, Statslab, University of Cambridge (Lent, 2018)
  • Coding lab demonstrator, APTS, Statistical computing module for Statistics PhD students, University of Cambridge (December, 2017)
  • Coding lab demonstrator, MLSALT1 graduate course, University of Cambridge (Michaelmas, 2017)
  • Coding lab demonstrator, 3F8 undergraduate course, University of Cambridge (Lent, 2017)
  • Teaching assistant, Statistical Data Mining and Machine Learning MSc in Applied Statistics course, University of Oxford (Hilary term 2014 and 2015)
  • Coding lab demonstrator, Kernel methods module, Introduction to machine learning graduate course, University College London (2013)
  • Teaching assistant, Probabilistic and Unsupervised Learning graduate course, University College London (Autumn, 2012)
  • Lecturer, Stochastic Processes, undergraduate course, Instituto Tecnológico Autónomo de México (Summer, 2011)


  • 2023 Action Editor, ACL RR
  • 2019, 2021, 2022 Uncertainty in Artificial Intelligence conference
  • 2018, 2020 Bayesian Analysis
  • 2018 Journal of Machine Learning Research
  • 2017 Biometrika
  • 2017 Scandinavian Journal of Statistics
  • 2016 Computational Statistics and Data Analysis
  • 2016 Statistics and Computing
  • 2016, 2017, 2019, 2024 International Conference in Machine Learning
  • 2013, 2014, 2015, 2017, 2018, 2019, 2023 Neural Information Processing Systems
  • 2014, 2015, 2020, 2021, 2022 AISTATS


    I was one of the organisers of our CSML Lunch Talk Series.